Visualization of arrhythmia detection by machine learning

ABSTRACT

Techniques are disclosed for explaining and visualizing an output of a machine learning system that detects cardiac arrhythmia in a patient. In one example, a computing device receives cardiac electrogram data sensed by a medical device. The computing device applies a machine learning model, trained using cardiac electrogram data for a plurality of patients, to the received cardiac electrogram data to determine, based on the machine learning model, that an episode of arrhythmia has occurred in the patient and a level of confidence in the determination that the episode of arrhythmia has occurred in the patient. In response to determining that the level of confidence is greater than a predetermined threshold, the computing device displays, to a user, a portion of the cardiac electrogram data, an indication that the episode of arrhythmia has occurred, and an indication of the level of confidence that the episode of arrhythmia has occurred.

This application is a continuation of U.S. patent application Ser. No.16/850,749, filed Apr. 16, 2020, which claims the benefit of U.S.Provisional Application No. 62/843,730, filed May 6, 2019. The entirecontent of Application No. 62/843,730 and application Ser. No.16/850,749 are incorporated herein by reference.

FIELD

This disclosure generally relates to medical devices.

BACKGROUND

Malignant tachyarrhythmia, for example, ventricular fibrillation, is anuncoordinated contraction of the cardiac muscle of the ventricles in theheart, and is the most commonly identified arrhythmia in cardiac arrestpatients. If this arrhythmia continues for more than a few seconds, itmay result in cardiogenic shock and cessation of effective bloodcirculation. Consequently, sudden cardiac death (SCD) may result in amatter of minutes.

In patients with a high risk of ventricular fibrillation, the use of animplantable medical device (IMD), such as an implantable cardioverterdefibrillator (ICD), has been shown to be beneficial at preventing SCD.An ICD is a battery powered electrical shock device, that may include anelectrical housing electrode (sometimes referred to as a can electrode),that is typically coupled to one or more electrical lead wires placedwithin the heart. If an arrhythmia is sensed, the ICD may send a pulsevia the electrical lead wires to shock the heart and restore its normalrhythm. Some ICDs have been configured to attempt to terminate detectedtachyarrhythmias by delivery of anti-tachycardia pacing (ATP) prior todelivery of a shock. Additionally, ICDs have been configured to deliverrelatively high magnitude post-shock pacing after successful terminationof a tachyarrhythmia with a shock, in order to support the heart as itrecovers from the shock. Some ICDs also deliver bradycardia pacing,cardiac resynchronization therapy (CRT), or other forms of pacing.

Other types of medical devices may be used for diagnostic purposes. Forinstance, an implanted or non-implanted medical device may monitor apatient's heart. A user, such as a physician, may review data generatedby the medical device for occurrences of cardiac arrythmias, e.g.,atrial or ventricular tachyarrhythmia, or asystole. The user maydiagnose a medical condition of the patient based on the identifiedoccurrences of the cardiac arrythmias.

SUMMARY

In accordance with the techniques of the disclosure, a medical devicesystem is set forth herein that explains and visualizes an output of amachine learning system that detects cardiac arrythmia in a patient.Machine learning systems may provide powerful tools for detecting andclassifying episodes of arrythmia in a patient. However, the foundationsfor the conclusions drawn by such machine learning systems may bedifficult to convey to a non-expert. Techniques are disclosed herein forsimplifying the conclusions drawn by a machine learning system withrespect to the detection of cardiac arrythmia in a patient andpresenting such information in a manner that is comprehensible to usersof differing ability, including subject matter experts and non-expertsalike.

In one example, a computing device receives cardiac electrogram datasensed by a medical device. The computing device applies a machinelearning model, trained using cardiac electrogram data for a pluralityof patients, to the received cardiac electrogram data to determine,based on the machine learning model, that an episode of arrhythmia hasoccurred in the patient and a level of confidence in the determinationthat the episode of arrhythmia has occurred in the patient. In responseto determining that the level of confidence is greater than apredetermined threshold, the computing device displays, to a user, aportion of the cardiac electrogram data, an indication that the episodeof arrhythmia has occurred, and an indication of the level of confidencethat the episode of arrhythmia has occurred. In some examples, thecomputing device provides more detailed information to advanced usersand less detailed information to basic users.

In another example, the computing device receives cardiac electrogramdata sensed by the medical device and a selection of an arrythmia typefrom a user. The computing device applies the machine learning model tothe received cardiac electrogram data to determine, based on the machinelearning model, that an episode of arrhythmia of the selected type hasoccurred in the patient and a level of confidence in the determinationthat the episode of arrhythmia of the selected type has occurred. Thecomputing device outputs, for display, at least a portion of the cardiacelectrogram data, a first indication that the episode of arrhythmia ofthe selected type has occurred in the patient, and a second indicationof the level of confidence that the episode of arrhythmia of theselected type has occurred in the patient.

The techniques of the disclosure may provide specific improvements tothe field of machine learning systems that perform cardiac arrythmiadetection and classification. For example, the techniques disclosedherein may allow for more clear explainability and visualization of theanalysis performed by such machine learning systems. Further, thetechniques described herein may allow for quick and patient-specificinterpretation of cardiac electrogram data for use by users of manydifferent skill levels. The techniques disclosed herein may reduce theamount of training required by users to make use of conclusions providedby machine learning systems that perform cardiac arrythmia detection andclassification, as well as enable the use of such machine learningsystems in a wider variety of systems. Accordingly, the techniquesdisclosed herein may enable more accurate and faster diagnosis andclassification of cardiac arrythmia in patients, while reducing theamount of expertise required by clinicians to diagnose and providetherapy for such cardiac arrythmia.

In one example, this disclosure describes a method comprising:receiving, by a computing device comprising processing circuitry and astorage medium, cardiac electrogram data sensed by a medical device;applying, by the computing device, a machine learning model, trainedusing cardiac electrogram data for a plurality of patients, to thereceived cardiac electrogram data to: determine, based on the machinelearning model, that an episode of arrhythmia has occurred in thepatient; and determine a level of confidence in the determination thatthe episode of arrhythmia has occurred in the patient; determining thatthe level of confidence in the determination that the episode ofarrhythmia has occurred in the patient is greater than a predeterminedthreshold; and in response to determining that the level of confidenceis greater than the predetermined threshold, outputting, by thecomputing device and for display to a user, at least a portion of thecardiac electrogram data, a first indication that the episode ofarrhythmia has occurred in the patient, and a second indication of thelevel of confidence that the episode of arrhythmia has occurred in thepatient.

In another example, this disclosure describes a method comprising:receiving, by a computing device comprising processing circuitry and astorage medium, cardiac electrogram data sensed by a medical device;receiving, from the user, a selection of an arrythmia type; applying, bythe computing device, a machine learning model, trained using cardiacelectrogram data for a plurality of patients, to the received cardiacelectrogram data to: determine, based on the machine learning model,that an episode of arrhythmia of the selected type has occurred in thepatient; and determine a level of confidence in the determination thatthe episode of arrhythmia of the selected type has occurred in thepatient; and outputting, by the computing device and for display to auser, at least a portion of the cardiac electrogram data, a firstindication that the episode of arrhythmia of the selected type hasoccurred in the patient, and a second indication of the level ofconfidence that the episode of arrhythmia of the selected type hasoccurred in the patient.

In another example, this disclosure describes a computing devicecomprising: a storage medium; and processing circuitry operable coupledto the storage medium and configured to: receive cardiac electrogramdata sensed by a medical device; apply a machine learning model, trainedusing cardiac electrogram data for a plurality of patients, to thereceived cardiac electrogram data to: determine, based on the machinelearning model, that an episode of arrhythmia has occurred in thepatient; and determine a level of confidence in the determination thatthe episode of arrhythmia has occurred in the patient; determine thatthe level of confidence in the determination that the episode ofarrhythmia has occurred in the patient is greater than a predeterminedthreshold; and in response to determining that the level of confidenceis greater than the predetermined threshold, output, for display to auser, at least a portion of the cardiac electrogram data, a firstindication that the episode of arrhythmia has occurred in the patient,and a second indication of the level of confidence that the episode ofarrhythmia has occurred in the patient.

In another example, this disclosure describes a computing devicecomprising: a storage medium; and processing circuitry operable coupledto the storage medium and configured to: receive cardiac electrogramdata sensed by a medical device; receive, from a user, a selection of anarrythmia type; apply a machine learning model, trained using cardiacelectrogram data for a plurality of patients, to the received cardiacelectrogram data to: determine, based on the machine learning model,that an episode of arrhythmia of the selected type has occurred in thepatient; and determine a level of confidence in the determination thatthe episode of arrhythmia of the selected type has occurred in thepatient; and output, for display to a user, at least a portion of thecardiac electrogram data, a first indication that the episode ofarrhythmia of the selected type has occurred in the patient, and asecond indication of the level of confidence that the episode ofarrhythmia of the selected type has occurred in the patient.

This summary is intended to provide an overview of the subject matterdescribed in this disclosure. It is not intended to provide an exclusiveor exhaustive explanation of the apparatus and methods described indetail within the accompanying drawings and description below. Furtherdetails of one or more examples are set forth in the accompanyingdrawings and the description below.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a conceptual drawing illustrating an example of a medicaldevice system for explaining detection and classification of cardiacarrhythmia including an implantable medical device and an externaldevice in conjunction with a patient in accordance with the techniquesof the disclosure.

FIG. 2 is a block diagram illustrating an example of the implantablemedical device of FIG. 1 .

FIG. 3 is a block diagram illustrating another example of theimplantable medical device of FIG. 1 .

FIG. 4 is a block diagram illustrating an example computing device thatoperates in accordance with one or more techniques of the presentdisclosure.

FIG. 5 is a flowchart illustrating an example operation in accordancewith the techniques of the disclosure.

FIG. 6 is a flowchart illustrating an example operation in accordancewith the techniques of the disclosure.

FIG. 7 is a flowchart illustrating an example operation in accordancewith the techniques of the disclosure.

FIG. 8 is a graph illustrating example simulated cardiac electrogramdata that may be used to explain a machine learning system in accordancewith the techniques of the disclosure.

FIGS. 9A-9C are graphs illustrating techniques for visualizing theoperation of machine learning model 150 of FIG. in detecting an episodeof arrythmia in accordance with the techniques of the disclosure.

FIGS. 10A-10D are illustrations depicting an example display forvisualizing cardiac electrogram data of a patient by a medical device inaccordance with the techniques of the disclosure.

FIGS. 11A-11C are illustrations depicting another example display forvisualizing cardiac electrogram data of a patient by a medical device inaccordance with the techniques of the disclosure.

Like reference characters refer to like elements throughout the figuresand description.

DETAILED DESCRIPTION

Machine learning systems, such as deep learning and artificialintelligence (AI), that perform arrythmia detection provide a flexibleplatform to develop algorithms with different objectives. For example,machine learning systems may be used to detect atrial fibrillation (AF)segments, detect the presence of AF, or detect other types of cardiacarrythmia. Further, such machine learning systems may be implementedwithout expert design and feature engineering that may be required byother techniques, such as feature delineation. However, the conclusionsdrawn by such machine learning systems, as well as the data drawn uponto make such conclusions, may be difficult to explain, hinderingevaluation of the performance of the machine learning system.

Techniques are disclosed for explaining and visualizing an output of amachine learning system that detects cardiac arrythmia in a patient. Thetechniques of the disclosure may allow for a medical device system toexplain the arrythmia classification by a machine learning model. Forexample, a system as described below may provide explainability andinterpretability of a machine learning system that performs arrhythmiadetection. Furthermore, a medical device system as described herein maypresent arrhythmias detected by the machine learning system for quickand patient-specific interpretation, and present such detectedarrhythmias to a variety of end-users who may have different levels offamiliarity and expertise with interpretation of cardiac electrogramdata. Such a medical device system as described herein may provide clearexplainability and simple arrhythmia visualization of a machine learningsystem, which may be useful as consumer and medical devices that cancollect and display cardiac electrogram data proliferate.

FIG. 1 is a conceptual drawing illustrating an example medical devicesystem 2 for explaining detection and classification of cardiacarrhythmia in a heart 6 of patient 4 including IMD 10 and externaldevice 12 in conjunction with a patient in accordance with thetechniques of the disclosure. In some examples, IMD 10 is a leadless IMDand is in wireless communication with external device 12, as illustratedin FIG. 1 . In some examples, IMD 10 may be coupled to one or moreleads. In some examples, IMD 10 may be implanted outside of a thoraciccavity of patient 4 (e.g., subcutaneously in the pectoral locationillustrated in FIG. 1 ). IMD 10 may be positioned near the sternum nearand/or just below the level of heart 6.

In some examples, IMD 10 may take the form of a Reveal LINQ™ InsertableCardiac Monitor (ICM) or a Holter Heart Monitor, both available fromMedtronic plc, of Dublin, Ireland. External device 12 may be a computingdevice configured for use in settings such as a home, clinic, orhospital, and may further be configured to communicate with IMD 10 viawireless telemetry. For example, external device 12 may be coupled tocomputing system 24 via network 25. Computing system 24 may include aremote patient monitoring system, such as Carelink®, available fromMedtronic plc, of Dublin, Ireland. External device 12 may, in someexamples, comprise a communication device such as a programmer, anexternal monitor, or a mobile device, such as a mobile phone, a “smart”phone, a laptop, a tablet computer, a personal digital assistant (PDA),etc.

In some examples, the example techniques and systems described hereinmay be used with an external medical device in addition to, or insteadof IMD 10. In some examples, the external medical device is a wearableelectronic device, such as the SEEQ™ Mobile Cardiac Telemetry (MCT)system available from Medtronic plc, of Dublin, Ireland, or another typeof wearable “smart” electronic apparel, such as a “smart” watch, “smart”patch, or “smart” glasses. Such an external medical device may bepositioned externally to patient 4 (e.g., positioned on the skin ofpatient 4) and may carry out any or all of the functions describedherein with respect to IMD 10.

In some examples, a user, such as a physician, technician, surgeon,electro-physiologist, or other clinician, may interact with externaldevice 12 to retrieve physiological or diagnostic information from IMD10. In some examples, a user, such as patient 4 or a clinician asdescribed above, may also interact with external device 12 to programIMD 10, e.g., select or adjust values for operational parameters of IMD10. In some examples, external device 12 acts as an access point tofacilitate communication with IMD 10 via network 25, e.g., by computingsystem 24. Computing system 24 may comprise computing devices configuredto allow a user to interact with IMD 10 via network 25.

In some examples, computing system 24 includes at least one of ahandheld computing device, computer workstation, server or othernetworked computing device, smartphone, tablet, or external programmerthat includes a user interface for presenting information to andreceiving input from a user. In some examples, computing system 24 mayinclude one or more devices that implement a machine learning system150, such as neural network, a deep learning system, or other type ofpredictive analytics system. A user, such as a physician, technician,surgeon, electro-physiologist, or other clinician, may interact withcomputing system 24 to retrieve physiological or diagnostic informationfrom IMD 10. A user may also interact with computing system 24 toprogram IMD 10, e.g., select values for operational parameters of theIMD. Computing system 24 may include a processor configured to evaluateEGM and/or other sensed signals transmitted from IMD 10 to computingsystem 24.

Network 25 may include one or more computing devices (not shown), suchas one or more non-edge switches, routers, hubs, gateways, securitydevices such as firewalls, intrusion detection, and/or intrusionprevention devices, servers, computer terminals, laptops, printers,databases, wireless mobile devices such as cellular phones or personaldigital assistants, wireless access points, bridges, cable modems,application accelerators, or other network devices. Network 25 mayinclude one or more networks administered by service providers, and maythus form part of a large-scale public network infrastructure, e.g., theInternet. Network 25 may provide computing devices, such as computingsystem 24 and IMD 10, access to the Internet, and may provide acommunication framework that allows the computing devices to communicatewith one another. In some examples, network 25 may be a private networkthat provides a communication framework that allows computing system 24,IMD 10, and/or external device 12 to communicate with one another butisolates one or more of computing system 24, IMD 10, or external device12 from devices external to network 25 for security purposes. In someexamples, the communications between computing system 24, IMD 10, andexternal device 12 are encrypted.

External device 12 and computing system 24 may communicate via wirelesscommunication over network 25 using any techniques known in the art. Insome examples, computing system 24 is a remote device that communicateswith external device 12 via an intermediary device located in network25, such as a local access point, wireless router, or gateway. While inthe example of FIG. 1 , external device 12 and computing system 24communicate over network 25, in some examples, external device 12 andcomputing system 24 communicate with one another directly. Examples ofcommunication techniques may include, for example, communicationaccording to the Bluetooth® or BLE protocols. Other communicationtechniques are also contemplated. Computing system 24 may alsocommunicate with one or more other external devices using a number ofknown communication techniques, both wired and wireless.

In any such examples, processing circuitry of medical device system 2may transmit patient data, including cardiac electrogram data, forpatient 4 to a remote computer (e.g., external device 12). In someexamples, processing circuitry of medical device system 2 may transmit adetermination that patient 4 is undergoing an episode of cardiacarrythmia such as an episode of bradycardia, tachycardia, atrialfibrillation, ventricular fibrillation, or AV Block.

External device 12 may be a computing device (e.g., used in a home,ambulatory, clinic, or hospital setting) to communicate with IMD 10 viawireless telemetry. External device 12 may include or be coupled to aremote patient monitoring system, such as Carelink®, available fromMedtronic plc, of Dublin, Ireland. In some examples, external device 12may receive data, alerts, patient physiological information, or otherinformation from IMD 10.

External device 12 may be used to program commands or operatingparameters into IMD 10 for controlling its functioning (e.g., whenconfigured as a programmer for IMD 10). In some examples, externaldevice 12 may be used to interrogate IMD 10 to retrieve data, includingdevice operational data as well as physiological data accumulated in IMDmemory. Such interrogation may occur automatically according to aschedule and/or may occur in response to a remote or local user command.Programmers, external monitors, and consumer devices are examples ofexternal devices 12 that may be used to interrogate IMD 10. Examples ofcommunication techniques used by IMD 10 and external device 12 includeradiofrequency (RF) telemetry, which may be an RF link established viaBluetooth, WiFi, or medical implant communication service (MICS). Insome examples, external device 12 may include a user interfaceconfigured to allow patient 4, a clinician, or another user to remotelyinteract with IMD 10. In some such examples, external device 12, and/orany other device of medical device system 2, may be a wearable device,(e.g., in the form of a watch, necklace, or other wearable item).

Medical device system 2 is an example of a medical device systemconfigured to perform cardiac arrhythmia detection, verification, andreporting. In accordance with the techniques of the disclosure, medicaldevice system 2 implements machine learning arrhythmia detection todetect and classify cardiac arrythmias in patient 4. Additional examplesof the one or more other implanted or external devices may include animplanted, multi-channel cardiac pacemaker, ICD, IPG, leadless (e.g.,intracardiac) pacemaker, extravascular pacemaker and/or ICD, or otherIMD or combination of such IMDs configured to deliver CRT to heart 6, anexternal monitor, an external therapy delivery device such as anexternal pacing or electrical stimulation device, or a drug pump.

Communication circuitry of each of the devices of medical device system2 (e.g., IMD 10 and external device 12) may enable the devices tocommunicate with one another. In addition, although one or more sensors(e.g., electrodes) are described herein as being positioned on a housingof IMD 10, in other examples, such sensors may be positioned on ahousing of another device implanted in or external to patient 4. In suchexamples, one or more of the other devices may include processingcircuitry configured to receive signals from the electrodes or othersensors on the respective devices and/or communication circuitryconfigured to transmit the signals from the electrodes or other sensorsto another device (e.g., external device 12) or server.

In accordance with the techniques of the disclosure, medical devicesystem 2 explains and visualizes an output of machine learning system150 that detects cardiac arrythmia in a patient. Machine learning system150 may provide tools for detecting and classifying episodes ofarrythmia in patient 4. However, the foundations for the conclusionsdrawn by machine learning system 150 may be difficult to convey. Asdiscussed in more detail below, medical device system 2 functions tosimplify the conclusions drawn by machine learning system 150 withrespect to the detection of cardiac arrythmia in patient 4. Further,medical device system 2 presents such information in a manner that iscomprehensible to users of differing ability, including subject matterexperts and non-experts alike.

In one example, computing system 24 receives cardiac electrogram datasensed by a medical device, such as one of IMD 10 and external device12. Computing system 24 applies a machine learning model of machinelearning system 150, trained using cardiac electrogram data for aplurality of patients, to the received cardiac electrogram data todetermine, based on the machine learning model, that an episode ofarrhythmia has occurred in patient 4. Machine learning system 150further determines a level of confidence in the determination that theepisode of arrhythmia has occurred in patient 4. In response todetermining that the level of confidence is greater than a predeterminedthreshold, computing system 24 displays, to a user, a portion of thecardiac electrogram data, an indication that the episode of arrhythmiahas occurred, and an indication of the level of confidence that theepisode of arrhythmia has occurred. In some examples, computing system24 provides more detailed information to advanced users and lessdetailed information to basic users.

In another example, computing system 24 receives cardiac electrogramdata sensed by, e.g., IMD 10, and a selection of an arrythmia type froma user. Computing system 24 applies machine learning system 150 to thereceived cardiac electrogram data to determine, based on the machinelearning model of machine learning system 150, that an episode ofarrhythmia of the selected type has occurred in patient 4 and a level ofconfidence in the determination that the episode of arrhythmia of theselected type has occurred. Computing system 24 outputs, for display, atleast a portion of the cardiac electrogram data, a first indication thatthe episode of arrhythmia of the selected type has occurred in patient4, and a second indication of the level of confidence that the episodeof arrhythmia of the selected type has occurred in patient 4.

The techniques of the disclosure may provide specific improvements tothe field of machine learning systems that perform cardiac arrythmiadetection and classification. For example, the techniques disclosedherein may allow for more clear explainability and visualization of theanalysis performed by machine learning system 150. Further, thetechniques described herein may allow for quick and patient-specificinterpretation of cardiac electrogram data for use by users of manydifferent skill levels. The techniques disclosed herein may reduce theamount of training required by users to make use of conclusions providedby machine learning systems that perform cardiac arrythmia detection andclassification, as well as enable the use of such machine learningsystems in a wider variety of systems. Accordingly, the techniquesdisclosed herein may enable more accurate and faster diagnosis andclassification of cardiac arrythmia in patients, while reducing theamount of expertise required by clinicians to diagnose and providetherapy for such cardiac arrythmia.

FIG. 2 is a block diagram illustrating an example of the leadlessimplantable medical device of FIG. 1 . As shown in FIG. 2 , IMD 10includes processing circuitry 50 sensing circuitry 52, communicationcircuitry 54, memory 56, sensors 58, switching circuitry 60, andelectrodes 16A, 16B (hereinafter “electrodes 16”), one or more of whichmay be disposed within a housing of IMD 10. In some examples, memory 56includes computer-readable instructions that, when executed byprocessing circuitry 50, cause IMD 10 and processing circuitry 50 toperform various functions attributed to IMD 10 and processing circuitry50 herein. Memory 56 may include any volatile, non-volatile, magnetic,optical, or electrical media, such as a random-access memory (RAM),read-only memory (ROM), non-volatile RAM (NVRAM), electrically-erasableprogrammable ROM (EEPROM), flash memory, or any other digital media.

Processing circuitry 50 may include fixed function circuitry and/orprogrammable processing circuitry. Processing circuitry 50 may includeany one or more of a microprocessor, a controller, a digital signalprocessor (DSP), an application specific integrated circuit (ASIC), afield-programmable gate array (FPGA), or equivalent discrete or analoglogic circuitry. In some examples, processing circuitry 50 may includemultiple components, such as any combination of one or moremicroprocessors, one or more controllers, one or more DSPs, one or moreASICs, or one or more FPGAs, as well as other discrete or integratedlogic circuitry. The functions attributed to processing circuitry 50herein may be embodied as software, firmware, hardware or anycombination thereof.

Sensing circuitry 52 and communication circuitry 54 may be selectivelycoupled to electrodes 16A, 16B via switching circuitry 60 as controlledby processing circuitry 50. Sensing circuitry 52 may monitor signalsfrom electrodes 16A, 16B in order to monitor electrical activity of aheart of patient 4 of FIG. 1 and produce cardiac electrogram data forpatient 4. Sensing circuitry 52 may produce a digitized version of thecardiac electrogram as well as indications of the timing ofdepolarizations. In some examples, processing circuitry 50 may performfeature delineation of the sensed cardiac electrogram data to detect anepisode of cardiac arrythmia of patient 4. In some examples, processingcircuitry 50 transmits, via communication circuitry 54, the cardiacelectrogram data for patient 4 to an external device, such as externaldevice 12 of FIG. 1 . For example, IMD 10 sends digitized cardiacelectrogram data to network 25 for processing by machine learning system150 of FIG. 1 . In some examples, IMD 10 transmits one or more segmentsof the cardiac electrogram data in response to detecting, via featuredelineation, an episode of arrythmia. In another example, IMD 10transmits one or more segments of the cardiac electrogram data inresponse to instructions from external device 12 (e.g., when patient 4experiences one or more symptoms of arrythmia and inputs a command toexternal device 12 instructing IMD 10 to upload the cardiac electrogramdata for analysis by a monitoring center or clinician). The cardiacelectrogram data may be processed by machine learning system 150 todetect and classify cardiac arrythmia as described in detail below.

In some examples, IMD 10 includes one or more sensors 58, such as one ormore accelerometers, microphones, and/or pressure sensors. Sensingcircuitry 52 may monitor signals from sensors 58 and transmit patientdata obtained from sensors 58, to an external device, such as externaldevice 12 of FIG. 1 , for analysis. In some examples, sensing circuitry52 may include one or more filters and amplifiers for filtering andamplifying signals received from one or more of electrodes 16A, 16Band/or other sensors 58. In some examples, sensing circuitry 52 and/orprocessing circuitry 50 may include a rectifier, filter and/oramplifier, a sense amplifier, comparator, and/or analog-to-digitalconverter.

Communication circuitry 54 may include any suitable hardware, firmware,software or any combination thereof for communicating with anotherdevice, such as external device 12 or another medical device or sensor,such as a pressure sensing device. Under the control of processingcircuitry 50, communication circuitry 54 may receive downlink telemetryfrom, as well as send uplink telemetry to, external device 12 or anotherdevice with the aid of an internal or external antenna, e.g., antenna26. In some examples, communication circuitry 54 may communicate withexternal device 12. In addition, processing circuitry 50 may communicatewith a networked computing device via an external device (e.g., externaldevice 12) and a computer network, such as the Medtronic CareLink®Network developed by Medtronic, plc, of Dublin, Ireland.

A clinician or other user may retrieve data from IMD 10 using externaldevice 12, or by using another local or networked computing deviceconfigured to communicate with processing circuitry 50 via communicationcircuitry 54. The clinician may also program parameters of IMD 10 usingexternal device 12 or another local or networked computing device. Insome examples, the clinician may select one or more parameters defininghow IMD 10 senses cardiac electrogram data of patient 4.

One or more components of IMD 10 may be coupled to a power source (notdepicted in FIG. 2 ), which may include a rechargeable ornon-rechargeable battery positioned within a housing of IMD 10. Anon-rechargeable battery may be selected to last for several years,while a rechargeable battery may be inductively charged from an externaldevice, e.g., on a daily or weekly basis.

In some examples, processing circuitry 50 senses cardiac electrogramdata of patient 4 via sensing circuitry 52 and uploads such cardiacelectrogram data to external device 12 of FIG. 1 . In some examples,processing circuitry 50 performs feature delineation of the sensedcardiac electrogram data to perform a preliminary detection of cardiacarrythmia, and only uploads the cardiac electrogram data of patient 4 toexternal device 12 in response to detecting an episode of cardiacarrythmia. In some examples, the feature delineation performed by IMD 10is of a reduced complexity so as to conserve power in IMD 10.

As described herein, feature delineation refers to the use of featuresobtained through signal processing for use in detecting or classifyingan episode cardiac arrythmia. Typically, feature delineation involvesthe use of engineered rules to identify or extract features in cardiacelectrogram data, measure characteristics of such features, and use themeasurements to detect or classify arrythmia. For example, featuredelineation may be used to identify features such as R-waves, QRScomplexes, P-waves, T-waves, rates of such features, intervals betweensuch features, feature morphology, widths or amplitudes of suchfeatures, or other or other types of cardiac features or characteristicsof such features not expressly described herein. Feature delineation mayinclude feature extraction, signal filtering, peak detection, refractoryanalysis, or other types of signal processing, feature engineering, ordetection rule development. Feature delineation algorithms may beoptimized for real-time, embedded, and low-power applications, such asfor use by an implantable medical device. However, feature delineationalgorithms may require expert design and feature engineering toaccurately detect arrythmia in a patient.

In contrast to feature delineation techniques for cardiac arrythmiadetection and classification, machine learning techniques may be usedfor cardiac arrythmia detection and classification. As described herein,machine learning refers the use of a machine learning model, such as aneural network or deep-learning model, that is trained on trainingdatasets to detect cardiac arrythmia from cardiac electrogram data.Machine learning techniques may be contrasted from feature delineationin that feature delineation relies on signal processing, which machinelearning systems may “learn” underlying features present in cardiacelectrogram data indicative of an episode of arrythmia without requiringknowledge or understanding of the relationship between the features andthe episode of arrythmia on behalf of the system designer.

Although described herein in the context of example IMD 10 that sensescardiac electrogram data of patient 4, the techniques for cardiacarrhythmia detection disclosed herein may be used with other types ofdevices. For example, the techniques may be implemented with anextra-cardiac defibrillator coupled to electrodes outside of thecardiovascular system, a transcatheter pacemaker configured forimplantation within the heart, such as the Micra™ transcatheter pacingsystem commercially available from Medtronic PLC of Dublin Ireland, aninsertable cardiac monitor, such as the Reveal LINQ™ ICM, alsocommercially available from Medtronic PLC, a neurostimulator, a drugdelivery device, a medical device external to patient 4, a wearabledevice such as a wearable cardioverter defibrillator, a fitness tracker,or other wearable device, a mobile device, such as a mobile phone, a“smart” phone, a laptop, a tablet computer, a personal digital assistant(PDA), or “smart” apparel such as “smart” glasses, a “smart” patch, or a“smart” watch.

FIG. 3 is a block diagram illustrating another example of the leadlessimplantable medical device of FIG. 1 . The components of FIG. 3 may notnecessarily be drawn to scale, but instead may be enlarged to showdetail. Specifically, FIG. 3 is a block diagram of a top view of anexample configuration of an IMD 10 of FIG. 1 .

FIG. 3 is a conceptual drawing illustrating an example IMD 10 that mayinclude components substantially similar to IMD 10 of FIG. 1 . Inaddition to the components illustrated in FIGS. 1 and 2 , the example ofIMD 10 illustrated in FIG. 3 also may include a wafer-scale insulativecover 74, which may help insulate electrical signals passing betweenelectrodes 16A, 16B on housing 14 and processing circuitry 50. In someexamples, insulative cover 74 may be positioned over an open housing 14to form the housing for the components of IMD 10B. One or morecomponents of IMD 10B (e.g., antenna 26, processing circuitry 50,sensing circuitry 52, communication circuitry 54, and/or switchingcircuitry 60 may be formed on a bottom side of insulative cover 74, suchas by using flip-chip technology. Insulative cover 74 may be flippedonto housing 14. When flipped and placed onto housing 14, the componentsof IMD 10B formed on the bottom side of insulative cover 74 may bepositioned in a gap 76 defined by housing 14. Housing 14 may be formedfrom titanium or any other suitable material (e.g., a biocompatiblematerial), and may have a thickness of about 200 micrometers to about500 micrometers. These materials and dimensions are examples only, andother materials and other thicknesses are possible for devices of thisdisclosure.

In some examples, IMD 10 collects, via sensing circuitry 52 and/orsensors 58, patient data of patient 4 including cardiac electrogramdata. Sensors 58 may include one or more sensors, such as one or moreaccelerometers, pressure sensors, optical sensors for O2 saturation,etc. In some examples, the patient data includes one or more of anactivity level of the patient, a heartrate of the patient, a posture ofthe patient, a cardiac electrogram of the patient, a blood pressure ofthe patient, accelerometer data for the patient, or other types ofpatient parametric data. IMD 10 uploads, via communication circuitry 54,the patient data to external device 12, which may in turn upload suchdata to computing system 24 over network 25. In some examples, IMD 10uploads the patient data to computing system 24 on a daily basis. Insome examples, the patient data includes one or more values thatrepresent average measurements of patient 4 over a long-term time period(e.g., about 24 hours to about 48 hours). In this example, IMD 10 bothuploads the patient data to computing system 24 and performs short-termmonitoring of patient 4 (as described below). However, in otherexamples, the medical device that processes the patient data to detectand/or classify arrythmia in patient 4 is different from the medicaldevice that performs short-term monitoring of patient 4.

FIG. 4 is a block diagram illustrating an example computing device 400that operates in accordance with one or more techniques of the presentdisclosure. In one example, computing device 400 is an exampleimplementation of computing system 24 of FIG. 1 . In one example,computing device 400 includes processing circuitry 402 for executingapplications 424 that include machine learning system 450 or any otherapplications described herein. Although shown in FIG. 4 as a stand-alonecomputing device 400 for purposes of example, computing device 400 maybe any component or system that includes processing circuitry or othersuitable computing environment for executing software instructions and,for example, need not necessarily include one or more elements shown inFIG. 4 (e.g., input devices 404, communication circuitry 406, userinterface devices 410, or output devices 412; and in some examplescomponents such as storage device(s) 408 may not be co-located or in thesame chassis as other components). In some examples, computing device400 may be a cloud computing system distributed across a plurality ofdevices.

As shown in the example of FIG. 4 , computing device 400 includesprocessing circuitry 402, one or more input devices 404, communicationcircuitry 406, one or more storage devices 408, user interface (UI)device(s) 410, and one or more output devices 412. Computing device 400,in one example, further includes one or more application(s) 424 such asmachine learning system 450, and operating system 416 that areexecutable by computing device 400. Each of components 402, 404, 406,408, 410, and 412 are coupled (physically, communicatively, and/oroperatively) for inter-component communications. In some examples,communication channels 414 may include a system bus, a networkconnection, an inter-process communication data structure, or any othermethod for communicating data. As one example, components 402, 404, 406,408, 410, and 412 may be coupled by one or more communication channels414.

Processing circuitry 402, in one example, is configured to implementfunctionality and/or process instructions for execution within computingdevice 400. For example, processing circuitry 402 may be capable ofprocessing instructions stored in storage device 408. Examples ofprocessing circuitry 402 may include, any one or more of amicroprocessor, a controller, a digital signal processor (DSP), anapplication specific integrated circuit (ASIC), a field-programmablegate array (FPGA), or equivalent discrete or integrated logic circuitry.

One or more storage devices 408 may be configured to store informationwithin computing device 400 during operation. Storage device 408, insome examples, is described as a computer-readable storage medium. Insome examples, storage device 408 is a temporary memory, meaning that aprimary purpose of storage device 408 is not long-term storage. Storagedevice 408, in some examples, is described as a volatile memory, meaningthat storage device 408 does not maintain stored contents when thecomputer is turned off. Examples of volatile memories include randomaccess memories (RAM), dynamic random access memories (DRAM), staticrandom access memories (SRAM), and other forms of volatile memoriesknown in the art. In some examples, storage device 408 is used to storeprogram instructions for execution by processing circuitry 402. Storagedevice 408, in one example, is used by software or applications 424running on computing device 400 to temporarily store information duringprogram execution.

Storage devices 408, in some examples, also include one or morecomputer-readable storage media. Storage devices 408 may be configuredto store larger amounts of information than volatile memory. Storagedevices 408 may further be configured for long-term storage ofinformation. In some examples, storage devices 408 include non-volatilestorage elements. Examples of such non-volatile storage elements includemagnetic hard discs, optical discs, floppy discs, flash memories, orforms of electrically programmable memories (EPROM) or electricallyerasable and programmable (EEPROM) memories.

Computing device 400, in some examples, also includes communicationcircuitry 406. Computing device 400, in one example, utilizescommunication circuitry 406 to communicate with external devices, suchas IMD 10 and external device 12 of FIG. 1 . Communication circuitry 406may include a network interface card, such as an Ethernet card, anoptical transceiver, a radio frequency transceiver, or any other type ofdevice that can send and receive information. Other examples of suchnetwork interfaces may include 3G and WiFi radios.

Computing device 400, in one example, also includes one or more userinterface devices 410. User interface devices 410, in some examples, areconfigured to receive input from a user through tactile, audio, or videofeedback. Examples of user interface devices(s) 410 include apresence-sensitive display, a mouse, a keyboard, a voice responsivesystem, video camera, microphone or any other type of device fordetecting a command from a user. In some examples, a presence-sensitivedisplay includes a touch-sensitive screen.

One or more output devices 412 may also be included in computing device400. Output device 412, in some examples, is configured to provideoutput to a user using tactile, audio, or video stimuli. Output device412, in one example, includes a presence-sensitive display, a soundcard, a video graphics adapter card, or any other type of device forconverting a signal into an appropriate form understandable to humans ormachines. Additional examples of output devices 412 include a speaker, acathode ray tube (CRT) monitor, a liquid crystal display (LCD), or anyother type of device that can generate intelligible output to a user.

Computing device 400 may include operating system 416. Operating system416, in some examples, controls the operation of components of computingdevice 400. For example, operating system 416, in one example,facilitates the communication of one or more applications 424 andlong-term prediction module 450 with processing circuitry 402,communication circuitry 406, storage device 408, input device 404, userinterface devices 410, and output device 412.

Application(s) 422 may also include program instructions and/or datathat are executable by computing device 400. Example application(s) 422executable by computing device 400 may include machine learning system450. Other additional applications not shown may alternatively oradditionally be included to provide other functionality described hereinand are not depicted for the sake of simplicity.

In accordance with the techniques of the disclosure, computing device400 applies a machine learning model of machine learning system 450 topatient data sensed by IMD 10 to detect and classify an episode ofarrythmia occurring in patient 10. In some examples, machine learningsystem 450 is an example of machine learning system 150 of FIG. 1 .

In some examples, the machine learning model implemented by machinelearning system 450 is trained with training data that comprises cardiacelectrogram data for a plurality of patients labeled with descriptivemetadata. For example, during a training phase, machine learning system450 processes a plurality of ECG waveforms. Typically, the plurality ofECG waveforms are from a plurality of different patients. Each ECGwaveform is labeled with one or more episodes of arrhythmia of one ormore types. For example, a training ECG waveform may include a pluralityof segments, each segment labeled with a descriptor that specifies anabsence of arrhythmia or a presence of an arrythmia of a particularclassification (e.g., bradycardia, tachycardia, atrial fibrillation,ventricular fibrillation, or AV Block). In some examples, a clinicianlabels the presence of arrythmia in each ECG waveform by hand. In someexamples, the presence of arrythmia in each ECG waveform is labeledaccording to classification by a feature delineation algorithm. Machinelearning system 450 may operate to convert the training data intovectors and tensors (e.g., multi-dimensional arrays) upon which machinelearning system 450 may apply mathematical operations, such as linearalgebraic, nonlinear, or alternative computation operations. Machinelearning system 450 uses the training data 104 to teach the machinelearning model to weigh different features depicted in the cardiacelectrogram data. In some examples, machine learning system 450 uses thecardiac electrogram data to teach the machine learning model to applydifferent coefficients that represent one or more features in a cardiacelectrogram as having more or less importance with respect to anoccurrence of a cardiac arrythmia of a particular classification. Byprocessing numerous such ECG waveforms labeled with episodes ofarrhythmia, machine learning system 450 may build and train a machinelearning model to receive cardiac electrogram data from a patient, suchas patient 4 of FIG. 1 , that machine learning system 450 has notpreviously analyzed, and process such cardiac electrogram data to detectthe presence or absence of arrythmia of different classifications in thepatient with a high degree of accuracy. Typically, the greater theamount of cardiac electrogram data on which machine learning system 450is trained, the higher the accuracy of the machine learning model indetecting or classifying cardiac arrhythmia in new cardiac electrogramdata.

After machine learning system 450 has trained the machine learningmodel, machine learning system 450 may receive patient data, such ascardiac electrogram data, for a particular patient, such as patient 4.Machine learning system 450 applies the trained machine learning modelto the patient data to detect an occurrence of an episode of cardiacarrythmia in patient 4. Further, machine learning system 450 applies thetrained machine learning model to the patient data to classify theepisode of cardiac arrythmia in patient as indicative of a particulartype of arrythmia. In some examples, machine learning system 450 mayoutput a determination that the episode of cardiac arrythmia isindicative of a particular type of arrythmia, as well as a level ofconfidence in the determination. In response to determining that thelevel of confidence in the determination is greater than a predeterminedthreshold (e.g., 50%, 75%, 90%, 95%, 99%), computing device 400 mayclassify that the episode of cardiac arrythmia as the particular type ofarrythmia and output, for display to a user, at least a portion of thecardiac electrogram data (e.g., a portion of an ECG during which theepisode of arrythmia occurred), a first indication that the episode ofarrythmia has occurred in patient 4, and a second indication of thelevel of confidence in the determination that the episode of arrythmiahas occurred.

In some examples, machine learning system 150 may process one or morecardiac features of cardiac electrogram data instead of the raw cardiacelectrogram data itself. The one or more cardiac features may beobtained via feature delineation performed by IMD 10, as describedabove. The cardiac features may include, e.g., one or more of a meanheartrate of the patient, a minimum heartrate of the patient, a maximumheartrate of the patient, a PR interval of a heart of the patient, avariability of heartrate of the patient, one or more amplitudes of oneor more features of an electrocardiogram (ECG) of the patient, or aninterval between the or more features of the ECG of the patient, aT-wave alternans, QRS morphology measures, or other types of cardiacfeatures not expressly described herein. In such exampleimplementations, machine learning system may train the machine learningmodel via a plurality of training cardiac features labeled with episodesof arrhythmia, instead of the plurality of ECG waveforms labeled withepisodes of arrhythmia as described above.

In some examples, machine learning system 450 may process the cardiacelectrogram data to derive a classification of the episode of arrythmia(e.g., bradycardia, tachycardia, atrial fibrillation, ventricularfibrillation, or AV Block). Further, machine learning system 450 maydetermine, for each of arrhythmia type classification, class activationdata indicating varying likelihoods of the classification over theperiod of time. For a given arrhythmia type, an amplitude of suchlikelihood values at different times corresponds to a probability thatan arrythmia is occurring at that time, with higher values correspondingto higher probability.

Computing device 400 may use class activation mapping to identifyregions of an input time series, e.g., of cardiac EGM data, thatconstitute the reason for the time series being given a particularclassification by the machine learning model of machine learning system450. A class activation map for a given classification may be aunivariate time series where each element (e.g., at each timestamp atthe sampling frequency of the input time series) may be a weighted sumor other value derived from the outputs of an intermediate layer of aneural network or other machine learning model. The intermediate layermay be a global average pooling layer and/or last layer prior to theoutput layer neurons for each classification.

In some examples, machine learning system 450 may apply the machinelearning model to other types of data to determine that an episode ofarrythmia has occurred in patient 4. For example, machine learningsystem 450 may apply the machine learning model to one or morecharacteristics of cardiac electrogram data that are correlated toarrhythmia in the patient, an activity level of IMD 10, an inputimpedance of IMD 10, or a battery level of IMD 10.

In further examples, processing circuitry 402 may generate, from thecardiac electrogram data, an intermediate representation of the cardiacelectrogram data. For example, processing circuitry 402 may apply one ormore signal processing, signal decomposition, wavelet decomposition,filtering, or noise reduction operations to the cardiac electrogram datato generate the intermediate representation of the cardiac electrogramdata. In this example, machine learning system 450 processes such anintermediate representation of the cardiac electrogram data to detectand classify an episode of arrythmia in patient 4. Furthermore, machinelearning system may train the machine learning model via a plurality oftraining intermediate representations labeled with episodes ofarrhythmia, instead of the plurality of raw ECG waveforms labeled withepisodes of arrhythmia as described above. The use of such intermediaterepresentations of the cardiac electrogram data may allow for thetraining and development of a lighter-weight, less computationallycomplex machine learning model by machine learning system 450. Further,the use of such intermediate representations of the cardiac electrogramdata may require less iterations and fewer training data to build anaccurate machine learning model, as opposed to the use of raw cardiacelectrogram data to train the machine learning model.

In some examples, computing system 24 may use machine learning system150 to detect other types of arrhythmias beyond the ones in detected inthe feature delineation screening analysis. For example, arrhythmiadetection algorithms for performing feature delineation implemented bylow-power devices such as IMD 10 may not be designed to detectless-frequently occurring arrhythmias, such as AV Blocks. Machinelearning system 150 may train a machine learning model on large datasetswhere such arrhythmias are available, thereby providing finergranularity and higher accuracy over feature delineation performed by,e.g., IMD 10 alone. Therefore, the use of machine learning system 150may expand the arrhythmia diagnosis capability of system 2 by allowingIMD 10 to implement a generic screening algorithm using featuredelineation followed by the use of machine learning system 150 thatimplements a machine learning model that can provide a wider range ofarrythmia detection. After detecting a type of arrythmia that was notdetected by feature delineation, computing system 24 may neverthelessuse feature delineation, such as QRS detection, to assist incharacterizing and reporting the other types of arrhythmias detected bythe machine learning model of machine learning system 150.

In some examples, computing system 24 may tailor machine learning system150 to the specific use case. For example, machine learning system 150may implement a machine learning model specific to detecting AV Blocksand bradycardia where patient 4 is a post-TAVR patient. As anotherexample, machine learning system 150 may implement a machine learningmodel specific to detecting PVCs such that PVC burden may be used torisk-stratify patients who might be indicated for ICDs.

FIG. 5 is a flowchart illustrating an example operation in accordancewith the techniques of the disclosure. For convenience, FIG. 5 isdescribed with respect to FIG. 1 . In some examples, the operation ofFIG. 5 is an operation for explaining and visualizing an output ofmachine learning system 150 that detects an episode of cardiac arrythmiain patient 4.

As depicted in FIG. 5 , IMD 10 senses cardiac electrogram data ofpatient 4. The cardiac electrogram data can be, e.g., an episodic ECG ofpatient 4 or a full-disclosure ECG of patient 4. Further, the cardiacelectrogram data of patient 4 may be from a single-channel ormulti-channel system. For simplicity, in the example of FIG. 5 , thecardiac electrogram data of patient 4 is described as single-channelepisodic ECG data. IMD 10 uploads the cardiac electrogram data toexternal device 12. Computing system 24 receives the cardiac electrogramdata from external device 12 (502).

Machine learning system 150 of computing system 24 applies a machinelearning model to the received cardiac electrogram data to detect anepisode of arrhythmia in patient 4 (504). In some examples, the machinelearning model is trained with a plurality of ECG episodes annotated bya clinician or a monitoring center for arrythmias of several differenttypes. In one example, machine learning system 150 applies the machinelearning model to one or several subsegments of a normalized input ECGsignal and generates arrhythmia labels and a likelihood of an occurrenceof the arrythmia. In some examples, machine learning system 150determines that an episode of arrhythmia has occurred in patient 4 anddetermines a level of confidence in the determination that the episodeof arrhythmia has occurred in patient 4. In some examples, machinelearning system 150 determines whether an episode of arrhythmia of aplurality of different arrythmia types has occurred in patient 4, aswell as a level of confidence that an episode of arrythmia of eacharrythmia type has occurred.

Computing system 24 determines whether machine learning system 150 hasdetected an episode of arrythmia (506). In response to determining thatmachine learning system 150 has detected an episode of arrythmia (e.g.,“YES” block of 506), computing system 24 determines whether level ofconfidence in the determination is greater than a predeterminedthreshold (510). In some examples, the predetermined threshold is, e.g.,25%, 50%, 75%, 90%, 95%, 99%, etc. in some examples, computing system 24determines whether the level of confidence in the determination isgreater than a first predetermined threshold (e.g., 50%) and whether thelevel of confidence in the determination is greater than a secondpredetermined threshold (e.g., 90%). The first predetermined thresholdmay be associated with a medium level of confidence by machine learningsystem 150 that the episode of arrythmia has occurred, while the secondpredetermined threshold may be associated with a high level ofconfidence by machine learning system 150.

In response to determining that the level of confidence is greater thanthe predetermined threshold (e.g., “YES” block of 510), computing system24 outputs the cardiac electrogram data for review by a clinician. Insome examples, computing system 24 outputs a portion of the cardiacelectrogram data, a first indication that the episode of arrythmia hasoccurred in patient 4, and a second indication of the level of certaintyin the determination that the episode of arrythmia has occurred inpatient 4 (512). In some examples, computing system 24 selects avisualization method according to the level of confidence by machinelearning system 150 that the episode of arrythmia has occurred. Forexample, computing system 24 may apply color coding to indicate results(e.g., “green” for low confidence than an episode of arrythmia hasoccurred, “yellow” for medium confidence than an episode of arrythmiahas occurred, or “red” for a high confidence than an episode ofarrythmia has occurred).

In some examples, computing system 24 uses different visualizationtechniques to indicate a type of arrythmia. In some examples, computingsystem 24 presents an ECG waveform and an annotation to the waveform toindicate where the episode of arrythmia has occurred. In some examples,the annotation includes highlighting a section of the ECG, indicating astart and/or stop time of the episode of arrythmia, or applying agraphical icon or text to the section of the ECG. Computing system 24may use a wide variety of different visualization techniques, such ascolor-coding, hatching, images or icons, shapes, indicators of differentsize, light, sound, textual notifications, etc. to simply theinformation conveyed to the user.

In one example, computing system 24 displays an indication that theepisode of arrhythmia has occurred in the patient and one or more of thecardiac features that coincide with the episode of arrythmia. In someexamples, computing system 24 displays a classification of the episodeof arrhythmia as a particular type of arrythmia.

In some examples, computing system 24 displays a subsection of thecardiac electrogram data obtained from patient 4 that coincides with theepisode of arrhythmia. For example, computing system 24 may identify asubsection of the cardiac electrogram data of patient 4, wherein thesubsection comprises cardiac electrogram data for a first time periodprior to the episode of arrhythmia (e.g., typically less than 10 minutesprior to the onset of the episode of arrhythmia), a second time periodduring the occurrence of the episode of arrhythmia, and a third timeperiod after the episode of arrhythmia (e.g., typically less than 10minutes after the cessation of the episode of arrhythmia).

As an example, a subsection of the cardiac electrogram data of patient 4may be about 6 seconds in length and includes representative segmentsbefore, during, and after an episode of arrythmia (if present in thecardiac electrogram data or waveform that is analyzed). In someexamples, the episode duration differs by device type, and may furtherdepend on a use case for the medical device, one or more settings of themedical device, or a particular type of arrhythmia sensed. For example,some types of arrhythmia self-terminate quickly, (resulting in a shortduration episode), while other types of arrythmia are sustained and of alength such that the recorded duration of the episode may depend on adesignated memory space on the medical device. As an example, for atrialfibrillation (AF), the subsection of the cardiac electrogram data ofpatient 4 may include cardiac electrogram data during an onset timeperiod, a segment of maximum AF likelihood, a segment of fastest AFrate, and an AF offset. Typically, a length of time of the cardiacelectrogram data of the patient is greater than the first, second, andthird time periods. Further, computing system 24 identifies one or moreof the cardiac features that coincide with the first, second, and thirdtime periods. computing system 24 displays the subsection of the cardiacelectrogram data and the one or more of the cardiac features thatcoincide with the first, second, and third time periods.

In response to determining that machine learning system 150 has notdetected an episode of arrythmia (e.g., “NO” block of 506), or inresponse to determining that the level of confidence is not greater thanthe predetermined threshold (e.g., “NO” block of 510), computing system24 archives the sensed cardiac electrogram data for review by amonitoring center or clinician at a later time (508).

FIG. 6 is a flowchart illustrating an example operation in accordancewith the techniques of the disclosure. For convenience, FIG. 6 isdescribed with respect to FIG. 1 . In some examples, the operation ofFIG. 6 is an operation for explaining and visualizing an output ofmachine learning system 150 that detects an episode of cardiac arrythmiaof a type selected by a user.

The operation of FIG. 6 may be similar to the operation of FIG. 5 inthat computing system 24 presents a visualization, such as a color-codeddiagram, of detected arrhythmias and a corresponding confidence levelthat each arrythmia is present. However, the operation of FIG. 6 allowsa user to pre-select an arrhythmia of a specific type or classification.In response, computing system 24 filters the output to depict thelikelihood of the presence of only the selected type(s) of arrhythmia ona location within the cardiac electrogram data, as well as acorresponding confidence level in the detection.

Computing system 24 receives the cardiac electrogram data from externaldevice 12 (602). Machine learning system 150 of computing system 24applies a machine learning model to the received cardiac electrogramdata to detect an episode of arrhythmia in patient 4 (604). Theoperation of steps 602 and 604 may occur in a substantially similarfashion as steps 502 and 504 of FIG. 5 , respectively.

Computing system 24 receives, from a user, a selection of a type ofarrythmia (605) for the specific patient. For example, computing systemmay receive, as an input from a user via an interface of computingsystem 24, a selection of an arrythmia such as bradycardia, tachycardia,atrial fibrillation, ventricular fibrillation, or AV Block. Computingsystem 24 determines whether machine learning system 150 detects anepisode of arrythmia of the selected type (606) in the current andsubsequent episodes from the specific patient. For example, in responseto determining that machine learning system 150 has detected no episodesof arrythmia of the selected type (e.g., “NO” block of 606), computingsystem 24 archives the sensed cardiac electrogram data for review by amonitoring center or clinician at a later time (608).

In response to determining that machine learning system 150 has detectedat least one episode of arrythmia of the selected type (e.g., “YES”block of 606), computing system 24 determines a level of confidence bymachine learning system 150 that the episode of arrythmia of theselected type has occurred in patient 4 (610). Furthermore, computingsystem 24 outputs a portion of the cardiac electrogram data, a firstindication that the episode of arrythmia has occurred in patient 4, anda second indication of the level of certainty in the determination thatthe episode of arrythmia has occurred in patient 4 (612).

Accordingly, the operation of FIG. 6 allows a user to select a specificarrhythmia of interest. Computing system 24 may update the visualpresentation based upon the user selection. Because computing system 24updates its user interface based upon user input, in addition todisplaying any arrhythmias detected with high and medium confidencelevels, computing system 24 may also present potential episodes ofarrhythmias that have been detected with a low confidence. In someexamples where an arrythmia is detected with a low confidence in thedetection, an indicator that the determination is of the low confidenceis prominently noted together with the corresponding value of the lowconfidence level in the detected arrhythmias. Thus, computing system 24may present a visualization or explanation of arrythmias of manydifferent types (and certainties). Accordingly, a clinician may usecomputing system 24 to confirm a classification by the clinician of anarrhythmia of a particular type. This may increase the accuracy indiagnosis of patient 4 by a clinician, particularly for arrythmias oftypes that are less prevalent and more difficult for clinicians toidentify. Computing system 24 may further help identify subsequentoccurrences of episodes of arrythmia of previously-identified types.

In another example, computing system 24 receives, from a user, aclassification of an episode of arrythmia as being of a particular typeof arrythmia. Machine learning system 150 uses the receivedclassification to train or update the machine learning model or thearrhythmia threshold to increase the accuracy and performance of machinelearning model 150. This may allow for increasing the accuracy andperformance of machine learning system 150 in detecting and classifyingepisodes of arrythmias of types that are difficult to detect, episodesof arrythmias of types that are dependent on a unique medical diagnosisparticular to a particular patient, or episodes of arrythmias of typesfor which data is scarce, uncommon, or of low prevalence, or ofarrhythmias whose detection performance in a specific patient might besub-optimal due to factors such as device position, change inphysiological state, etc.

FIG. 7 is a flowchart illustrating an example operation in accordancewith the techniques of the disclosure. For convenience, FIG. 7 isdescribed with respect to FIG. 1 . In some examples, the operation ofFIG. 7 is an operation for explaining and visualizing an output ofmachine learning system 150 that detects an episode of cardiac arrythmiain patient 4.

Computing system 24 receives the cardiac electrogram data from externaldevice 12 (702). Machine learning system 150 of computing system 24applies a machine learning model to the received cardiac electrogramdata to detect an episode of arrhythmia in patient 4 (704). Computingsystem 24 determines whether an episode of arrythmia is detected (706),and a level of confidence in the detection (710). The operation of steps702, 704, 706, 708, and 710 may occur in a substantially similar fashionas steps 502, 504, 506, 508, and 510 of FIG. 5 , respectively.

Furthermore, computing system 24 determines whether a user type isadvanced (714).

In this example, computing system 24 may customize the visualization ofthe cardiac electrogram data based on the ability of the user. Forexample, computing system 24 may present more- or less-detailed ECGwaveform, metadata, and resulting analysis provided by the AI as neededfor a variety of users of different abilities.

For example, in response to determining that the user type is notadvanced (e.g., a basic user type) (e.g., “NO” block of 714), computingsystem 24 outputs basic cardiac electrogram data, a first indicationthat the episode of arrythmia has occurred in patient 4, and a secondindication of the level of certainty in the determination that theepisode of arrythmia has occurred in patient 4 (716). For example, fornon-experts such as implanting cardiologists, neurologists, HFphysicians, device nurses, patients/caregivers, computing system 24 maypresent for display an overall performance of machine learning system150 in terms of a “true and false” positive detection of an episode ofarrythmia. In some examples, computing system 24 may further present,for display, an ECG segment depicting the detected episode of arrhythmiaoverlaid with a representative episode of arrythmia of the sameclassification (e.g., an ECG segment of patient 4 presenting AF overlaidwith a representative example waveform of AF). In some examples,computing system 24 may further present, for display, an ECG segmentdepicting the detected episode of arrhythmia overlaid with a baseline ornon-AF episode. In some examples, computing device 24 presents, to abasic user, an ECG waveform of the patient, a first representation of afirst ECG waveform presenting an episode of arrhythmia, and a secondrepresentation of a second ECG waveform presenting normal cardiacbehavior.

As another example, in response to determining that the user type isadvanced (e.g., “YES” block of 714), computing system 24 outputsadvanced cardiac electrogram data, the first indication that the episodeof arrythmia has occurred in patient 4, and the second indication of thelevel of certainty in the determination that the episode of arrythmiahas occurred in patient 4 (718). For example, for an electrophysiologistor subject matter expert, computing system 24 may display, e.g. for anepisode of AF, a start and stop time of each episode of arrhythmiapresented along with a mean RR during each AF segment contrasted againsta mean RR baseline, an RR variation during each AF segment contrastedagainst an RR variation baseline, P-wave evidence during the AF segmentcontrasted with a P-wave baseline, and morphology variation. Computingsystem 24 may present additional types of information not expresslydescribed herein to improve a confidence of the expert in machinelearning system 150 and to assist the expert in interpreting an episodeof cardiac arrythmia. In some examples, computing device 24 presents, toan advanced user, one or more of an ECG waveform of patient 4, a starttime of the episode of arrhythmia, a stop time of the episode ofarrhythmia, a mean R-R interval of patient 4 during the episode ofarrhythmia, an R-R variation of patient 4 during the episode ofarrhythmia, a baseline R-R interval of patient 4, a P-wave of patient 4during the episode of arrhythmia, a baseline P-wave of patient 4, or amorphology variation of patient 4.

Accordingly, the operation of FIG. 7 further allows for a different datapresentation based upon the skill, ability, or experience of the useraccessing the data. The operation of FIG. 7 may be used, e.g., insituations where a patient is prescribed a cardiac monitor by aphysician stakeholder that is less familiar with interpretation ofcardiac waveforms (e.g., a clinician who is not a cardiologist orsubject matter expert), but whom still wants to see a basic presentationof the characteristics of the cardiac electrogram data and/or theclassification by machine learning system 150. By using the techniquesof the disclosure, a medical device system such as medical device system2 may provide an end-user with an appropriate amount of data. Thus,medical device system 2 may enable a clinician to make an appropriatediagnosis or referral, or to confirm the presence or absence of anexpected cardiac rhythm (or arrythmia). Furthermore, medical devicesystem 2 may avoid burdening a clinician with data irrelevant to theirrequired level of understanding.

FIG. 8 is a graph illustrating example simulated cardiac electrogramdata that may be used to explain machine learning system 150 inaccordance with the techniques of the disclosure. For convenience, FIG.8 is described with respect to medical system 24 of FIG. 1 .

Unlike feature-engineered algorithms, it may not be clear exactly howmachine learning system 150 operates to detect arrythmia from cardiacelectrogram data of a patient. As described herein, computing system 24may use simulated cardiac electrogram data, such as waveforms 802, 804,and 806, to probe different aspects of the arrythmia characterizationperformed by machine learning system 150. For example, computing system24 may feed simulated cardiac electrogram data across a plurality ofdifferent characteristics to machine learning system 150 and map theoutput of machine learning system 150 (as described in more detail belowwith respect to FIGS. 9A-9C) to understand how the machine learningmodel weighs the different characteristics as more or less importantwith respect to detecting arrythmia of a particular type. For example,computing system 24 may use simulated cardiac electrogram data that hasdiffering values for RR variability (RRV), RR rate, or p-waves toexamine how machine learning system 150 characterizes AF. As describedherein, “RRV” refers to a variation in the interval between successive“R” points corresponding to peak of a QRS complex of an ECG wave. Byusing the simulated cardiac electrogram data and the determination bythe machine learning system 150 of a likelihood of arrythmia fordifferent portions of the simulated cardiac electrogram data, computingsystem 24 may explain the operation of machine learning system 150.While examples specific to Normal Sinus Rhythm (NSR), Bradyarrhythmia,and AF are described herein, the techniques of the disclosure may beused for other types of arrythmia as well.

In one example, computing system 24 receives a dataset of waveforms withthe following characteristics:

-   -   Mean heart rate (HR) ranging from 40 beats per minute (BPM) to        120 BPM;    -   RRV for the waveform ranging from 0.01 seconds to 0.5 seconds;        and    -   QRS complex with p-waves and without p-waves.        In other words, computing system 24 “explains” the analysis of        machine learning system 150 based on characteristics of the        simulated cardiac electrogram data such as these, which may be        more understandable by experts in the field and have        “real-world” significance.

In some examples, computing system 24 may use simulated data for atleast a portion of the mean heart rate data, the RRV data, or the QRScomplex where such data is unavailable. For example, patient 4 may notexhibit an entire range of parameters required to explain the model(e.g, such as BPM less than 50). The use of such simulated data mayallow computing system 24 to explain the analysis of machine learningsystem 150 without requiring edge-case data from patient 4 that isdifficult or infeasible to obtain. In some examples, computing system 24may use real data for patient 4 where such data is available.

The example of FIG. 8 depicts 3 example waveforms 802, 804, and 806.Waveform 802 is a waveform with a mean heartrate of 90 BPM and an RRvariability of 0.01 seconds. Waveform 804 is a waveform with a meanheartrate of 90 BPM and an RR variability of 0.01 seconds. Waveform 806is a waveform with a mean heartrate of 90 BPM, an RR variability 0.5seconds and p-waves. In some examples, at least a portion of one or moreof waveforms 802, 804, and 806 may be obtained from simulated data.

Machine learning system 150 processes each of waveforms 802, 804, and806 and outputs a likelihood that an arrhythmia occurrence in the [0,1]range was extracted. A likelihood close to 0 indicates that an episodeof arrhythmia in the waveform is unlikely, and a likelihood close to 1that an episode of arrhythmia in the waveform is very likely. Computingsystem 24 may use such information to explain, e.g., at what heartratesmachine learning system 150 detects an episode of arrythmia of aparticular type (e.g., AF), at what RRV machine learning system 150detects an episode of arrythmia of a particular type (e.g., AF), or atwhat p-wave levels machine learning system 150 detects an episode ofarrythmia of a particular type (e.g., AF).

FIGS. 9A-9C are graphs illustrating techniques for visualizing theoperation of machine learning model 150 of FIG. in detecting an episodeof arrythmia in accordance with the techniques of the disclosure. Insome examples, FIGS. 9A-9C illustrate an explanation by computing system24 of an analysis by machine learning system 150 of the examplewaveforms 802, 804, and 806 of FIG. 8 . By presenting the output ofmachine learning system 150 with respect to the simulated cardiacelectrogram data of FIG. 8 , computing system 24 may explain theoperation of machine learning system 150 with respect to differentcharacteristics of the simulated cardiac electrogram data.

FIG. 9A depicts an example graphical illustration 900 of a likelihoodthat machine learning system 150 predicts a normal sinus rhythm (NSR) inpatient 4. For example, FIG. 9B depicts a likelihood that machinelearning system 150 detects NSR as a function of mean heartrate. They-axis of FIG. 9A depicts a likelihood determined by machine learningsystem 150 that NSR is present, on a scale of 0% to 100%. A color ofFIG. 9A may correspond to the value of the y-axis (e.g., with yellowcorresponding to high likelihood that NSR is present and bluecorresponding to low likelihood that NSR is present). The x-axis of FIG.9A depicts a heart rate in beats per minute (BPM), and the z-axis ofFIG. 9A depicts an RRV in seconds. As illustrated in FIG. 9A, machinelearning system 150 detects NSR where the cardiac electrogram has an RRVless than 0.1 and a heartrate between 65 and 85 BPM. A clinician may usesuch information to characterize how normal sinus rhythms in patient 4are annotated. For example, the machine learning model described in FIG.9A was developed with data from a specific clinic that considersheartrates between 65 and 85 BPM to be NSR.

FIG. 9B depicts an example graphical illustration 910 of a likelihoodthat machine learning system 150 detects bradycardia. For example, FIG.9B depicts a likelihood that machine learning system 150 detectsbradycardia as a function of mean heartrate. The y-axis of FIG. 9Bdepicts a likelihood determined by machine learning system 150 thatbradycardia is present, on a scale of 0% to 100%. A color of FIG. 9B maycorrespond to the value of the y-axis (e.g., with yellow correspondingto high likelihood that bradycardia is present and blue corresponding tolow likelihood that bradycardia is present). The x-axis of FIG. 9Bdepicts a heart rate in BPM, and the z-axis of FIG. 9B depicts an RRV inseconds. As illustrated in FIG. 9B, machine learning system 150 detectssinus bradycardia where the cardiac electrogram has an RRV less than 0.1and a heartrate between 45 and 55 BPM. A clinician may use suchinformation to characterize how episodes of bradycardia in patient 4 areannotated. For example, the machine learning model described in FIG. 9Bwas developed with data from a specific clinic that considers heartratesbetween 45 and 55 BPM to be sinus bradycardia.

FIG. 9C depicts an example graphical illustration 920 of a likelihoodthat machine learning system 150 detects atrial fibrillation (AF). Theexample of FIG. 9C depicts a likelihood that machine learning system 150detects AF a function of mean heartrate, RR variability and P-waves. They-axis of FIG. 9C depicts a likelihood determined by machine learningsystem 150 that AF is present on a scale of 0% to 100%. A color of FIG.9C may correspond to the value of the y-axis (e.g., with yellowcorresponding to high likelihood that AF is present and bluecorresponding to low likelihood that AF is present). The x-axis of FIG.9C depicts a heart rate in BPM, and the z-axis of FIG. 9C depicts RRV inseconds. Further, FIG. 9C depicts two scenarios: where p-waves arepresent (924) and where p-waves are absent (922). As illustrated in FIG.9C, machine learning system 150 detects AF where the cardiac electrogramhas an RRV greater than 0.2, a heartrate greater than 75 BPM, andp-waves are absent. In the presence of p-waves, machine learning system150 does not detect AF. While not depicted in FIG. 9C, machine learningsystem 150 may detect episodes of PAC more often in the presence ofp-waves. A clinician may use such information to characterize howepisodes of AF in patient 4 are annotated. For example, the machinelearning model described in FIG. 9C was developed with data from aspecific clinic that considers heartrates greater than 75 BPM and RRVgreater than 0.2 to be AF.

The example visualization techniques depicted by FIGS. 9A-9C may beextended by including other deep-learning visualization techniques. Forexample, the techniques of the disclosure may readily be adapted todeep-learning techniques for visualizing deep network features or forvisualizing neural style transfer. Furthermore, the techniques of thedisclosure may be adapted to visualize other types of cardiac arrythmianot expressly depicted in FIGS. 9A-9C, such as ventricle fibrillation orAV Block.

FIGS. 10A-10D are illustrations depicting example displays 1001-1004 forvisualizing cardiac electrogram data 1010 of patient 4 by a computingdevice in accordance with the techniques of the disclosure. Cardiacelectrogram data 1010 may be sensed by, e.g., liVID 10 as describedabove. Display 1000 may be presented, e.g., by computing system 24 or byexternal device 12. FIG. 10A depicts display 1001 presenting cardiacelectrogram data 1010 of patient 4 sensed by IMD 10.

FIG. 10B depicts display 1002 presenting basic cardiac information,e.g., ECG segment 1020 of cardiac electrogram data 1010 during whichcomputing system 24 has determined that an episode of arrythmia inpatient 4 has occurred. In some examples, ECG segment 1020 is a segmentof cardiac electrogram data 1010 in which computing system 24 hasdetermined is the highest likelihood as presenting an episode of atrialfibrillation in patient 4. The example presentation of FIG. 10B may beused, e.g., when a user is a basic user that does not need comprehensiveinformation regarding the determination of the episode of arrythmia bycomputing system 24.

FIG. 10C depicts display 1003 presenting advanced cardiac information.For example, display 1003 includes cardiac electrogram data 1010 andfurther depicts, for a plurality of segments of cardiac electrogram data1010, a likelihood that an episode of cardiac arrythmia of one or moretypes has occurred. For example, display 1003 colors segments for whichcomputing system 24 has determined a high likelihood that atrialfibrillation has occurred in patient 4 in green (1012), segments forwhich computing system 24 has made an uncertain determination of whetheratrial fibrillation has occurred in patient 4 in yellow (1014), andsegments for which computing system 24 has determined a low likelihoodthat atrial fibrillation has occurred in patient 4 in red (1016).Furthermore, display 1003 depicts an overall likelihood 1030 that atrialfibrillation has occurred in patient 4 over time in blue.

As another example, display 1003 colors segments for which computingsystem 24 has determined a high likelihood that PVC has occurred inpatient 4 in green (no episodes in FIG. 10C), segments for whichcomputing system 24 has made an uncertain determination of whether PVChas occurred in patient 4 in yellow (1024), and segments for whichcomputing system 24 has determined a low likelihood that PVC hasoccurred in patient 4 in red (1026). Furthermore, display 1003 depictsan overall likelihood 1040 that PVC has occurred in patient 4 over timein magenta.

FIG. 10D depicts display 1004 presenting advanced cardiac information.For example, display 1004 depicts information substantially similar todisplay 1003 of FIG. 10C. Further, display 1004 further depictsreference cardiac electrogram data 1054 that depicts a baseline ECGsignal (e.g., where no AF is present) and reference cardiac electrogramdata 1052 that depicts an ECG signal during an episode of AF.Furthermore, display 1004 includes RR interval diagram 1050, whichdisplays RR intervals of patient 4 over the duration of cardiacelectrogram data 1010. RR interval diagram 1050 demonstrates high,un-patterned RR variability during the presence of AF (e.g., from timet0 to about time t100) and patterned beats during a lack of AF (e.g.,from time t150 to time t250). The example presentations of FIG. 10C or10D may be used, e.g., when a user is an advanced basic user thatdesires comprehensive information regarding the determination of theepisode of arrythmia by computing system 24.

FIGS. 11A-11C are illustrations depicting an example displays 1101-1103for visualizing cardiac electrogram data 1110 of patient 4 by acomputing device in accordance with the techniques of the disclosure.Cardiac electrogram data 1110 may be sensed by, e.g., IMD 10 asdescribed above. Display 1100 may be presented, e.g., by computingsystem 24 or by external device 12. FIG. 11A depicts display 1101presenting cardiac electrogram data 1110 of patient 4 sensed by IMD 10.

FIG. 11B depicts display 1102 presenting basic cardiac information,e.g., ECG segment 1120 of cardiac electrogram data 1110 during whichcomputing system 24 has determined that an episode of arrythmia inpatient 4 has occurred. In some examples, ECG segment 1120 is a segmentof cardiac electrogram data 1110 in which computing system 24 hasdetermined is the highest likelihood as presenting an episode of PVC inpatient 4. The example presentation of FIG. 11B may be used, e.g., whena user is a basic user that does not need comprehensive informationregarding the determination of the episode of arrythmia by computingsystem 24.

FIG. 11C depicts display 1103 presenting advanced cardiac information.For example, display 1103 includes cardiac electrogram data 1110 andfurther depicts, for a plurality of segments of cardiac electrogram data1110, a likelihood that an episode of cardiac arrythmia of one or moretypes has occurred. For example, display 1103 colors segments for whichcomputing system 24 has determined a high likelihood that atrialfibrillation has occurred in patient 4 in green (not present in FIG.11C), segments for which computing system 24 has made an uncertaindetermination of whether atrial fibrillation has occurred in patient 4in yellow (not present in FIG. 11C), and segments for which computingsystem 24 has determined a low likelihood that atrial fibrillation hasoccurred in patient 4 in red (1116). Furthermore, display 1103 depictsan overall likelihood 1130 that atrial fibrillation has occurred inpatient 4 over time in blue.

As another example, display 1103 colors segments for which computingsystem 24 has determined a high likelihood that PVC has occurred inpatient 4 in green (1122), segments for which computing system 24 hasmade an uncertain determination of whether PVC has occurred in patient 4in yellow (not present in FIG. 11C), and segments for which computingsystem 24 has determined a low likelihood that PVC has occurred inpatient 4 in red (1126). Furthermore, display 1103 depicts an overalllikelihood 1140 that PVC has occurred in patient 4 over time in magenta.

The following examples may illustrate one or more aspects of thedisclosure.

Example 1. A method comprising: receiving, by a computing devicecomprising processing circuitry and a storage medium, cardiacelectrogram data sensed by a medical device; applying, by the computingdevice, a machine learning model, trained using cardiac electrogram datafor a plurality of patients, to the received cardiac electrogram datato: determine, based on the machine learning model, that an episode ofarrhythmia has occurred in the patient; and determine a level ofconfidence in the determination that the episode of arrhythmia hasoccurred in the patient; determining that the level of confidence in thedetermination that the episode of arrhythmia has occurred in the patientis greater than a predetermined threshold; and in response todetermining that the level of confidence is greater than thepredetermined threshold, outputting, by the computing device and fordisplay to a user, at least a portion of the cardiac electrogram data, afirst indication that the episode of arrhythmia has occurred in thepatient, and a second indication of the level of confidence that theepisode of arrhythmia has occurred in the patient.

Example 2. The method of example 1, wherein the at least a portion ofthe cardiac electrogram data comprises an electrocardiogram (ECG)waveform.

Example 3. The method of example 2, wherein the first indication thatthe episode of arrhythmia has occurred in the patient comprises anannotation to the ECG waveform.

Example 4. The method of any of examples 1 through 3, wherein the secondindication comprises one or more of a color, an image, a light, a sound,or a textual notification.

Example 5. The method of any of examples 1 through 4, wherein the methodfurther comprises receiving, from the user, a selection of an arrythmiatype, wherein applying the machine learning model to the receivedcardiac electrogram data to determine that an episode of arrhythmia hasoccurred in the patient comprises applying the machine learning model tothe received cardiac electrogram data to determine that an episode ofarrhythmia of the selected arrhythmia type has occurred in the patient,and wherein outputting the at least a portion of the cardiac electrogramdata, the first indicator that the episode of arrhythmia has occurred inthe patient, and the second indicator of the level of confidence thatthe episode of arrhythmia has occurred in the patient comprisesoutputting the at least a portion of the cardiac electrogram data, afirst indicator that the episode of arrhythmia of the selected arrythmiatype has occurred in the patient, and the second indicator of the levelof confidence that the episode of arrhythmia of the selected arrythmiatype has occurred in the patient.

Example 6. The method of any of examples 1 through 5, wherein the methodfurther comprises determining, by the computing device, that the user isa basic user, wherein, outputting the at least a portion of the cardiacelectrogram data, the first indicator that the episode of arrhythmia hasoccurred in the patient, and the second indicator of the level ofconfidence that the episode of arrhythmia has occurred in the patientcomprises outputting, in response to determining that the user is abasic user, the first indicator that the episode of arrhythmia hasoccurred in the patient, the second indicator of the level of confidencethat the episode of arrhythmia has occurred in the patient, and one ormore of: an electrocardiogram (ECG) waveform of the patient; a firstrepresentation of a first ECG waveform presenting an episode ofarrhythmia; and a second representation of a second ECG waveformpresenting normal cardiac behavior.

Example 7. The method any of examples 1 through 5: wherein the methodfurther comprises determining, by the computing device, that the user isan advanced user, wherein, outputting the at least a portion of thecardiac electrogram data, the first indicator that the episode ofarrhythmia has occurred in the patient, and the second indicator of thelevel of confidence that the episode of arrhythmia has occurred in thepatient comprises outputting, in response to determining that the useris an advanced user, the first indicator that the episode of arrhythmiahas occurred in the patient, the second indicator of the level ofconfidence that the episode of arrhythmia has occurred in the patient,and one or more of: an electrocardiogram (ECG) waveform of the patient;a start time of the episode of arrhythmia; a stop time of the episode ofarrhythmia; a mean R-R interval of the patient during the episode ofarrhythmia; an R-R variation of the patient during the episode ofarrhythmia; a baseline R-R interval of the patient; a P-wave of thepatient during the episode of arrhythmia; a baseline P-wave of thepatient; and a morphology variation of the patient.

Example 8. The method of any of examples 1 through 7, wherein theepisode of arrhythmia in the patient is at least one of an episode ofbradycardia, tachycardia, atrial fibrillation, ventricular fibrillation,or AV Block.

Example 9. The method of any of examples 1 through 8, wherein themachine learning model trained using cardiac electrogram data for theplurality of patients comprises a machine learning model trained using aplurality of electrocardiogram (ECG) waveforms, each ECG waveformlabeled with one or more episodes of arrhythmia in a patent of theplurality of patients.

Example 10. The method of any of examples 1 through 9, wherein applyingthe machine learning model to the received cardiac electrogram datacomprises applying the machine learning model to at least one of:electrocardiogram (ECG) data of the patient; the characteristicscorrelated to arrhythmia in the patient; a type of the arrhythmia in thepatient; an activity level of the implantable medical device; an inputimpedance of the implantable medical device; or a battery level of theimplantable medical device.

Example 11. The method of any of examples 1 through 10, whereinoutputting the at least a portion of the cardiac electrogram datacomprises: identifying a subsection of an electrocardiogram (ECG) of thepatient, wherein the subsection comprises ECG data for a first timeperiod prior to the episode of arrhythmia, a second time period duringthe episode of arrhythmia, and a third time period after the episode ofarrhythmia, and wherein a length of time of the ECG of the patient isgreater than the first, second, and third time periods; and outputtingthe subsection of the ECG.

Example 12. The method of any of examples 1 through 11, wherein thepredetermined threshold is a first predetermined threshold, and whereinthe method further comprises determining whether the level of confidencein the determination that the episode of arrhythmia has occurred in thepatient is greater than a second predetermined threshold, the secondpredetermined threshold greater than the first predetermined threshold,wherein in response to determining that the level of confidence isgreater than the predetermined threshold, outputting, the at least aportion of the cardiac electrogram data, the first indication, and thesecond indication comprises: in response to determining that the levelof confidence is greater than the first predetermined threshold but notgreater than the second predetermined threshold, outputting, the atleast a portion of the cardiac electrogram data, the first indication,and an indication of a medium level of confidence that the episode ofarrhythmia has occurred in the patient; in response to determining thatthe level of confidence is greater than the first predeterminedthreshold and greater than the second predetermined threshold,outputting, the at least a portion of the cardiac electrogram data, thefirst indication, and an indication of a high level of confidence thatthe episode of arrhythmia has occurred in the patient.

Example 13. A method comprising: receiving, by a computing devicecomprising processing circuitry and a storage medium, cardiacelectrogram data sensed by a medical device; receiving, from the user, aselection of an arrythmia type; applying, by the computing device, amachine learning model, trained using cardiac electrogram data for aplurality of patients, to the received cardiac electrogram data to:determine, based on the machine learning model, that an episode ofarrhythmia of the selected type has occurred in the patient; anddetermine a level of confidence in the determination that the episode ofarrhythmia of the selected type has occurred in the patient; andoutputting, by the computing device and for display to a user, at leasta portion of the cardiac electrogram data, a first indication that theepisode of arrhythmia of the selected type has occurred in the patient,and a second indication of the level of confidence that the episode ofarrhythmia of the selected type has occurred in the patient.

Example 14. The method of example 13, wherein the at least a portion ofthe cardiac electrogram data comprises an electrocardiogram (ECG)waveform.

Example 15. The method of example 14, wherein the first indication thatthe episode of arrhythmia has occurred in the patient comprises anannotation to the ECG waveform.

Example 16. The method of any of examples 13 through 15, wherein thesecond indication comprises one or more of a color, an image, a light, asound, or a textual notification.

Example 17. The method of any of examples 13 through 16, wherein themethod further comprises determining, by the computing device, that theuser is a basic user, wherein, outputting the at least a portion of thecardiac electrogram data, the first indicator that the episode ofarrhythmia has occurred in the patient, and the second indicator of thelevel of confidence that the episode of arrhythmia has occurred in thepatient comprises outputting, in response to determining that the useris a basic user, the first indicator that the episode of arrhythmia hasoccurred in the patient, the second indicator of the level of confidencethat the episode of arrhythmia has occurred in the patient, and one ormore of: an electrocardiogram (ECG) waveform of the patient; a firstrepresentation of a first ECG waveform presenting an episode ofarrhythmia; and a second representation of a second ECG waveformpresenting normal cardiac behavior.

Example 18. The method any of examples 13 through 16: wherein the methodfurther comprises determining, by the computing device, that the user isan advanced user, wherein, outputting the at least a portion of thecardiac electrogram data, the first indicator that the episode ofarrhythmia has occurred in the patient, and the second indicator of thelevel of confidence that the episode of arrhythmia has occurred in thepatient comprises outputting, in response to determining that the useris an advanced user, the first indicator that the episode of arrhythmiahas occurred in the patient, the second indicator of the level ofconfidence that the episode of arrhythmia has occurred in the patient,and one or more of: an electrocardiogram (ECG) waveform of the patient;a start time of the episode of arrhythmia; a stop time of the episode ofarrhythmia; a mean R-R interval of the patient during the episode ofarrhythmia; an R-R variation of the patient during the episode ofarrhythmia; a baseline R-R interval of the patient; a P-wave of thepatient during the episode of arrhythmia; a baseline P-wave of thepatient; and a morphology variation of the patient.

Example 19. The method of any of examples 13 through 18, wherein theepisode of arrhythmia in the patient is at least one of an episode ofbradycardia, tachycardia, atrial fibrillation, ventricular fibrillation,or AV Block.

Example 20. The method of any of examples 13 through 19, wherein themachine learning model trained using cardiac electrogram data for theplurality of patients comprises a machine learning model trained using aplurality of electrocardiogram (ECG) waveforms, each ECG waveformlabeled with one or more episodes of arrhythmia in a patient of theplurality of patients.

Example 21. The method of any of examples 13 through 20, whereinapplying the machine learning model to the received cardiac electrogramdata comprises applying the machine learning model to at least one of:electrocardiogram (ECG) data of the patient; the characteristicscorrelated to arrhythmia in the patient; a type of the arrhythmia in thepatient; an activity level of the implantable medical device; an inputimpedance of the implantable medical device; or a battery level of theimplantable medical device.

Example 22. The method of any of examples 1 through 21, whereinoutputting the at least a portion of the cardiac electrogram datacomprises: identifying a subsection of an electrocardiogram (ECG) of thepatient, wherein the subsection comprises ECG data for a first timeperiod prior to the episode of arrhythmia, a second time period duringthe episode of arrhythmia, and a third time period after the episode ofarrhythmia, and wherein a length of time of the ECG of the patient isgreater than the first, second, and third time periods; and outputtingthe subsection of the ECG.

In some examples, the techniques of the disclosure include a system thatcomprises means to perform any method described herein. In someexamples, the techniques of the disclosure include a computer-readablemedium comprising instructions that cause processing circuitry toperform any method described herein.

It should be understood that various aspects disclosed herein may becombined in different combinations than the combinations specificallypresented in the description and accompanying drawings. It should alsobe understood that, depending on the example, certain acts or events ofany of the processes or methods described herein may be performed in adifferent sequence, may be added, merged, or left out altogether (e.g.,all described acts or events may not be necessary to carry out thetechniques). In addition, while certain aspects of this disclosure aredescribed as being performed by a single module, unit, or circuit forpurposes of clarity, it should be understood that the techniques of thisdisclosure may be performed by a combination of units, modules, orcircuitry associated with, for example, a medical device.

In one or more examples, the described techniques may be implemented inhardware, software, firmware, or any combination thereof. If implementedin software, the functions may be stored as one or more instructions orcode on a computer-readable medium and executed by a hardware-basedprocessing unit. Computer-readable media may include non-transitorycomputer-readable media, which corresponds to a tangible medium such asdata storage media (e.g., RAM, ROM, EEPROM, flash memory, or any othermedium that can be used to store desired program code in the form ofinstructions or data structures and that can be accessed by a computer).

Instructions may be executed by one or more processors, such as one ormore digital signal processors (DSPs), general purpose microprocessors,application specific integrated circuits (ASICs), field programmablelogic arrays (FPGAs), or other equivalent integrated or discrete logiccircuitry. Accordingly, the term “processor” or “processing circuitry”as used herein may refer to any of the foregoing structure or any otherphysical structure suitable for implementation of the describedtechniques. Also, the techniques could be fully implemented in one ormore circuits or logic elements.

Various examples have been described. These and other examples arewithin the scope of the following claims.

What is claimed is:
 1. A computing device comprising: a storage medium;and processing circuitry operably coupled to the storage medium andconfigured to: receive electrocardiogram (ECG) data sensed by animplantable cardiac monitoring device (ICM); after receiving the ECGdata, receive user input from a user via a user input device, whereinthe user input corresponds to an arrhythmia type from a plurality ofdifferent arrhythmia types; apply a machine learning model, trainedusing ECG data for a plurality of patients, to the received ECG data to:determine, based on the machine learning model, that an episode ofarrhythmia of the arrhythmia type has occurred in a patient; anddetermine a level of confidence in the determination that the episode ofarrhythmia of the arrhythmia type has occurred in a patient; andgenerate data for display based on the user input and based on the levelof confidence determined by the machine learning model that used thesensed ECG data from the ICM, the data comprising an indication that theepisode of arrhythmia of the arrhythmia type has occurred in thepatient.
 2. The computing device of claim 1, wherein the data furthercomprises at least a portion of the received ECG data including an ECGwaveform, and wherein the indication that the episode of arrhythmia hasoccurred in the patient comprises an annotation to the ECG waveform,wherein the annotation to the ECG waveform visualizes informationdetermined by applying the machine learning model to the received ECGdata.
 3. The computing device of claim 1, wherein to generate the datafor display based on the user input and based on the level ofconfidence, the processing circuitry is further configured to include,in the data, the indication that the episode of arrhythmia of thearrhythmia type has occurred in the patient in response to the level ofconfidence exceeding a threshold, wherein the data further comprises anindication of the level of confidence.
 4. The computing device of claim1, wherein the processing circuitry is configured to: determine that theuser is a basic user; and in response to determining that the user isthe basic user, include in the data one or more of an ECG waveform ofthe patient, a first representation of a first ECG waveform presentingan episode of arrhythmia, or a second representation of a second ECGwaveform presenting normal cardiac behavior.
 5. The computing device ofclaim 1, wherein the processing circuitry is configured to: determinethat the user is an advanced user; in response to determining that theuser is the advanced user, include in the data one or more of: an ECGwaveform of the patient; a start time of the episode of arrhythmia; astop time of the episode of arrhythmia; a mean R-R interval of thepatient during the episode of arrhythmia; an R-R variation of thepatient during the episode of arrhythmia; a baseline R-R interval of thepatient; a P-wave of the patient during the episode of arrhythmia; abaseline P-wave of the patient; or a morphology variation of thepatient.
 6. The computing device of claim 1, wherein the plurality ofdifferent arrhythmia types includes at least two of bradycardia,tachycardia, atrial fibrillation, ventricular fibrillation, or AV Block.7. The computing device of claim 1, wherein the machine learning modeltrained using ECG data for the plurality of patients comprises a machinelearning model trained using a plurality of ECG waveforms, each ECGwaveform labeled with one or more episodes of arrhythmia in a patient ofthe plurality of patients.
 8. The computing device of claim 1, whereinto apply the machine learning model to the received ECG data, theprocessing circuitry is configured to apply the machine learning modelto at least one of: characteristics correlated to arrhythmia in thepatient; a type of the arrhythmia in the patient; an activity level ofthe implantable cardiac monitoring device; an input impedance of theimplantable cardiac monitoring device; or a battery level of theimplantable cardiac monitoring device.
 9. The computing device of claim1, wherein to generate the data for display, the processing circuitry isfurther configured to: identify a subsection of an ECG of the patientbased on applying the machine learning model to the received ECG data,wherein the subsection comprises ECG data for a first time period priorto the episode of arrhythmia, a second time period during the episode ofarrhythmia, and a third time period after the episode of arrhythmia, andwherein a length of time of the ECG of the patient is greater than thefirst, second, and third time periods; and include in the data thesubsection of the ECG.
 10. The computing device of claim 1, wherein theuser input sets a threshold, and wherein to generate the data fordisplay based on the user input and based on the level of confidence,the processing circuitry is further configured to include, in the data,the indication that the episode of arrhythmia of the selected type hasoccurred in the patient in response to the level of confidence exceedingthe threshold.
 11. The computing device of claim 1, wherein the userinput causes the processing circuitry to selectively display theindication that the episode of arrhythmia of the arrhythmia type hasoccurred in the patient based on the level of confidence determined bythe machine learning model that used the sensed ECG data from the ICM.12. The computing device of claim 1, wherein the processing circuitry isfurther configured to apply one or more visualization techniques to thedata based on an output of the machine learning model.
 13. The computingdevice of claim 12, wherein the one or more visualization techniquescomprise one or more of: applying color-coding to the data based on theoutput of the machine learning model, adding hatching, images, shapes,or icons to the data based on the output of the machine learning model,or determining sizes for visual representations of the data based on theoutput of the machine learning model.
 14. A computer readable storagemedium storing instructions that when executed by one or more processorscause the one or more processors to: receive electrocardiogram (ECG)data sensed by an implantable cardiac monitoring device (ICM); afterreceiving the ECG data, receive user input, from a user via a user inputdevice, wherein the user input corresponds to an arrhythmia type from aplurality of different arrhythmia types; apply a machine learning model,trained using ECG data for a plurality of patients, to ECG data sensedby an implantable cardiac monitoring device to: determine, based on themachine learning model, that an episode of arrhythmia of the arrhythmiatype has occurred in the patient; and determine a level of confidence inthe determination that the episode of arrhythmia of the arrhythmia typehas occurred in the patient; and output data for display based on theuser input and based on the level of confidence determined by themachine learning model that used the sensed ECG data from the ICM, thedata comprising an indication that the episode of arrhythmia of thearrhythmia type has occurred in the patient.
 15. The computer readablestorage medium of claim 14, wherein the data further comprises at leasta portion of the sensed ECG data including an ECG waveform, wherein theindication that the episode of arrhythmia has occurred in the patientcomprises an annotation to the ECG waveform, wherein the annotation tothe ECG waveform visualizes information determined by applying themachine learning model to the received ECG data.
 16. The computerreadable storage medium of claim 14, wherein to generate the data fordisplay based on the user input and based on the level of confidence,the instructions cause the one or more processors to include, in thedata, the indication that the episode of arrhythmia of the arrhythmiatype has occurred in the patient in response to the level of confidenceexceeding a threshold, wherein the data further comprises an indicationof the level of confidence.
 17. The computer readable storage medium ofclaim 14, wherein the instructions cause the one or more processors todetermine that the user is a basic user, and in response to determiningthat the user is the basic user, include in the data one or more of: anECG waveform of the patient; a first representation of a first ECGwaveform presenting an episode of arrhythmia; or a second representationof a second ECG waveform presenting normal cardiac behavior.
 18. Thecomputer readable storage medium of claim 14: wherein the instructionscause the one or more processors to determine that the user is anadvanced user, and in response to determining that the user is theadvanced user, include in the data one or more of: an ECG waveform ofthe patient; a start time of the episode of arrhythmia; a stop time ofthe episode of arrhythmia; a mean R-R interval of the patient during theepisode of arrhythmia; an R-R variation of the patient during theepisode of arrhythmia; a baseline R-R interval of the patient; a P-waveof the patient during the episode of arrhythmia; a baseline P-wave ofthe patient; and a morphology variation of the patient.
 19. The computerreadable storage medium of claim 14, wherein the plurality of differentarrhythmia types includes at least two of bradycardia, tachycardia,atrial fibrillation, ventricular fibrillation, or AV Block.
 20. Thecomputer readable storage medium of claim 14, wherein to output thedata, the instructions cause the one or more processors to: identify asubsection of the sensed ECG data based on applying the machine learningmodel to the received ECG data, wherein the subsection comprises ECGdata for a first time period prior to the episode of arrhythmia, asecond time period during the episode of arrhythmia, and a third timeperiod after the episode of arrhythmia, and wherein a length of time ofthe sensed ECG data of the patient is greater than the first, second,and third time periods; and output the subsection of the ECG.
 21. Thecomputer readable storage medium of claim 14, wherein the user inputsets a threshold, and wherein to generate the data for display based onthe user input and based on the level of confidence, the instructionscause the one or more processors to include, in the data, the indicationthat the episode of arrhythmia of the selected type has occurred in thepatient in response to the level of confidence exceeding the threshold.22. The computer readable storage medium of claim 14, wherein user inputcauses the instructions to cause the one or more processors toselectively display the indication that the episode of arrhythmia of thearrhythmia type has occurred in the patient based on the level ofconfidence determined by the machine learning model that used the sensedECG data from the ICM.
 23. A medical system comprising: an implantablecardiac monitoring (ICM) device configured to sense electrocardiogram(ECG) data; and processing circuitry operably coupled to a storagemedium and configured to: receive the ECG data sensed by the ICM device;after receiving the ECG data, receive user input from a user via a userinput device, wherein the user input corresponds to an arrhythmia typefrom a plurality of different arrhythmia types; apply a machine learningmodel, trained using ECG data for a plurality of patients, to thereceived ECG data to: determine, based on the machine learning model,that an episode of arrhythmia of the arrhythmia type has occurred in thepatient; and determine a level of confidence in the determination thatthe episode of arrhythmia of the arrhythmia type has occurred in thepatient; and output, based on the user input and based on the level ofconfidence determined by the machine learning model that used the sensedECG data from the ICM, data comprising an indication that the episode ofarrhythmia of the arrhythmia type has occurred in the patient.
 24. Themedical system of claim 23, wherein the plurality of differentarrhythmia types includes at least two of bradycardia, tachycardia,atrial fibrillation, ventricular fibrillation, or AV Block.
 25. Themedical system of claim 23, wherein the data comprises at least aportion of the received ECG data, and the processing circuitry isfurther configured to: identify a subsection of an ECG of the patientbased on applying the machine learning model to the received ECG data,wherein the subsection comprises ECG data for a first time period priorto the episode of arrhythmia, a second time period during the episode ofarrhythmia, and a third time period after the episode of arrhythmia, andwherein a length of time of the ECG of the patient is greater than thefirst, second, and third time periods; and include in the data thesubsection of the ECG.
 26. The medical system of claim 23, wherein theprocessing circuitry comprises processing circuitry of a computingdevice configured to communicate with the implantable cardiac monitoringdevice.
 27. The medical system of claim 23, wherein the processingcircuitry comprises processing circuitry of a cloud-based computingdevice.
 28. A medical system comprising: an implantable cardiacmonitoring (ICM) device configured to sense electrocardiogram (ECG)data; and processing circuitry operably coupled to a storage medium andconfigured to: receive the ECG data sensed by the ICM device; afterreceiving the ECG data, receive user input from a user via a user inputdevice, wherein the user input corresponds to an arrhythmia type from aplurality of different arrhythmia types; output for display, based onthe user input and based on a level of confidence determined by themachine learning model that used the sensed ECG data from the ICM, anindication that an episode of arrhythmia of the arrhythmia type hasoccurred in the patient; and means for applying the machine learningmodel, trained using ECG data for a plurality of patients, to thereceived ECG data to: determine, based on the machine learning model,that the episode of arrhythmia of the arrhythmia type has occurred inthe patient; and determine the level of confidence in the determinationthat the episode of arrhythmia of the arrhythmia type has occurred inthe patient.
 29. The medical system of claim 28, wherein the theplurality of different arrhythmia types includes at least two of anepisode of bradycardia, tachycardia, atrial fibrillation, ventricularfibrillation, or AV Block.
 30. The medical system of claim 28, whereinthe data further comprises a portion of the received ECG data, and theprocessing circuitry is further configured to: identify a subsection ofthe received ECG data based on applying the machine learning model tothe received ECG data, wherein the subsection comprises ECG data for afirst time period prior to the episode of arrhythmia, a second timeperiod during the episode of arrhythmia, and a third time period afterthe episode of arrhythmia, and wherein a length of time of the receivedECG data of the patient is greater than the first, second, and thirdtime periods; and include in the data the subsection of the received ECGdata.